CN110257518B - Gene set for predicting curative effect of metastatic colorectal cancer transformation treatment - Google Patents
Gene set for predicting curative effect of metastatic colorectal cancer transformation treatment Download PDFInfo
- Publication number
- CN110257518B CN110257518B CN201910583680.7A CN201910583680A CN110257518B CN 110257518 B CN110257518 B CN 110257518B CN 201910583680 A CN201910583680 A CN 201910583680A CN 110257518 B CN110257518 B CN 110257518B
- Authority
- CN
- China
- Prior art keywords
- transformation
- gene
- colorectal cancer
- therapy
- predicting
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
- C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
- C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
- G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/154—Methylation markers
Landscapes
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Engineering & Computer Science (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Analytical Chemistry (AREA)
- Genetics & Genomics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Physics & Mathematics (AREA)
- Organic Chemistry (AREA)
- Pathology (AREA)
- Zoology (AREA)
- Biophysics (AREA)
- Immunology (AREA)
- Biotechnology (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Wood Science & Technology (AREA)
- Microbiology (AREA)
- Biochemistry (AREA)
- Oncology (AREA)
- General Engineering & Computer Science (AREA)
- Hospice & Palliative Care (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Medical Informatics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Theoretical Computer Science (AREA)
- Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
Abstract
The invention discloses a gene set for predicting the curative effect of metastatic colorectal cancer transformation treatment, which comprises the following 10 gene loci in a human genome: c18orf42, OR10V1, ATOH1, AC140061.12, TMEM26, KRTAP4-2, PDK4, SPAG11B, SEC31B and LILRA5, wherein the apparent modification level of 5hmC of the gene sites indicates the curative effect of transformation therapy, and a mathematical model established by adopting the gene set can be used for predicting the curative effect of the transformation therapy of the metastatic colorectal cancer which can not be resected by a liver metastasis, and helps doctors to determine a transformation therapeutic scheme and implement precise medical treatment.
Description
Technical Field
The invention belongs to the field of gene detection, and particularly relates to a gene set for predicting the curative effect of metastatic colorectal cancer transformation treatment, and a model for predicting the curative effect of metastatic colorectal cancer transformation treatment based on the gene set.
Background
Colorectal cancer (CRC) is one of the most common malignancies worldwide. The incidence rate of colorectal cancer in China is 376.3/10 ten thousand, and the colorectal cancer is the fifth in all malignant tumors and shows a growing trend. Approximately 25% of colorectal cancer patients are diagnosed with concurrent liver metastasis at the first visit, and approximately 50% of colorectal cancer patients are ultimately diagnosed with colorectal cancer with liver metastasis as the sporadic liver metastasis occurs. Radical liver metastases resection is the preferred treatment for such patients, and the 5-year survival rate of colorectal cancer patients who receive radical resection for liver metastases exceeds 53%. However, median survival time for unresectable liver metastases was only 9.3 months. Unfortunately, only 10% -20% of patients at initial diagnosis have an opportunity to undergo radical surgical resection of liver metastases. In addition, about one third of patients who have undergone liver metastasis resection have intrahepatic recurrence. Liver metastases therefore significantly affect the long-term survival of patients with colorectal cancer, becoming a major cause of death in such patients.
For patients with colorectal cancer with liver metastasis, the patients should be actively treated with transformation therapy (or conversion therapy) to regress the tumor and achieve the purpose of radical resection. The application of molecular targeted drugs such as cetuximab, bevacizumab and the like remarkably improves the conversion and excision rate and long-term survival rate of patients with liver metastasis of colorectal cancer, and becomes one of important treatment means in precise medical treatment. In a clinical study at the secondary zhongshan hospital of the university of counterdenier, cetuximab in combination with chemotherapy increased the resection rate from 7.3% to 25.7% after transformation therapy in KRAS wild-type patients with initial unresectable concurrent liver metastases. Despite the progress of transformation therapy, the primary and secondary drug resistance (such as KRAS mutation) of molecular targeted drugs by liver metastases seriously affect the efficacy, the clinical effective rate is only about 30%, and unnecessary economic cost is increased, so that it is important to screen people who are sensitive to (or effective in) transformation therapy. In a research of subsidiary Zhongshan Hospital of the university of Fudan, a complete exon sequencing method is adopted to screen out related genes resistant to cetuximab, a curative effect prediction model of cetuximab is constructed, and the effective rate of cetuximab administration is improved to a certain extent. In addition, the research of verifying or predicting the curative effects of chemotherapeutic drugs and molecular targeted drugs by means of human-derived transplantable tumors and the like is reported at home and abroad, but the research has the defects of low tumor formation rate, long detection period and unstable detection process. Currently, research into screening of populations that benefit from transformation therapy is still under investigation.
With the progress of tumor research, the role of DNA apparent modification in tumorigenesis and development is gradually receiving attention. 5-methylcytosine (5-methylcytosine, 5mC or 5-mC) is formed by methylation of the fifth carbon in the cytosine loop of DNA by deoxynucleotide methyltransferase and plays an important role in mammalian embryonic development and disease development. Two science articles in 2009 discovered that TET enzyme can oxidize 5mC into 5-hydroxymethylcytosine (5-hydroxymethylyticine, 5hmC or 5-hmC) and play a physiologically important role, and 5hmC is also rapidly a hotspot in research and is called as "sixth DNA base". In the next few years scientists found that 5hmC, like 5mC, is an important epigenetic modification. The gene distribution of 5hmC can accurately correspond to the regulation and control of gene activity, and the gene expression state is more dynamically and sensitively reflected than that of 5 mC. In 2012, the article in the journal of Cell reports the close relationship between hydroxymethylcytosine and melanoma (Lian et al, 2012). There were subsequently several influential academic papers that further validated 5hmC as a marker for cell development, nervous system, tumor, and cardiovascular disease, among others. 5hmC is a promising epigenetic tumor marker. Like the epigenetic modification of 5mC, the method of high-throughput sequencing is combined to draw a gene map of 5hmC on the whole genome under a specific situation, which is particularly important. By knowing the distribution of hydroxymethylated cytosine in DNA on the genome, the regulatory information of the genome can be correspondingly analyzed. Research shows that the expression level of hydroxymethylation in a normal genome is relatively stable, and the hydroxymethylation has a unique apparent modification effect and is not only an intermediate product of 5-mC metabolism. 5hmC is associated with a variety of diseases including cancer, with low levels of 5hmC being demonstrated in a variety of tumor tissues. The level of 5hmC is closely related to the type and clinical stage of the tumor, for example, in lung cancer, the level of 5hmC is progressively reduced with the development of pathological stages. The applicant previously established an early diagnosis model of colorectal cancer by using 5hmC detection, and found that 5hmC has characteristic changes in patients with stage IV colorectal cancer.
The liquid biopsy of the tumor is rapidly developed due to the characteristics of no wound, convenience and the like, and has certain innovation on the prediction of the curative effect of the transformation treatment of the metastatic colorectal cancer by detecting the peripheral blood. As is well known in the medical field, human peripheral plasma is an important biological sample for detecting free DNA, the detection is convenient and safe, and the compliance of patients can be obviously improved. Therefore, the detection of the apparent modification characteristics of the gene 5hmC in body fluid, particularly peripheral plasma, has important clinical significance, and the established transformation therapy effectiveness prediction model can be used for judging colorectal cancer patients needing transformation therapy, guiding the administration of molecular targeted drugs and implementing precise medical treatment.
Disclosure of Invention
In order to make up the defects of a prediction model of the curative effect of the metastatic colorectal cancer transformation treatment in the prior art, achieve the purposes of accurate medical treatment, guarantee of the dosing accuracy of the metastatic colorectal cancer transformation treatment and reduction of the economic burden of a patient, the inventor finds a gene set for predicting the curative effect of the metastatic colorectal cancer transformation treatment, and can construct a prediction model of the curative effect of the metastatic colorectal cancer transformation treatment based on the gene set. Specifically, the present invention includes the following technical solutions.
A gene set for predicting the efficacy of a treatment for metastatic colorectal cancer transformation, comprising the following 10 loci in the human genome: c18orf42, OR10V1, ATOH1, AC140061.12, TMEM26, KRTAP4-2, PDK4, SPAG11B, SEC31B, LILRA5, with an apparent modified level of 5hmC at these loci predictive of efficacy of transformation therapy (including efficacy of transformation therapy, i.e., patient sensitivity to transformation therapy, primary resistance to transformation therapy, i.e., patient insensitivity to transformation therapy). These gene loci are modified with 5-hmC to form gene markers.
The biological sample for detecting the genome may be plasma, particularly peripheral venous plasma.
In one embodiment, the transformation therapy may be molecular targeted drug delivery.
Preferably, the above molecular targeting drug may be selected from cetuximab and bevacizumab.
The gene set can be used for establishing a prediction mathematical model of the curative effect of the transformation therapy of the metastatic colorectal cancer, and helps doctors to determine transformation therapy schemes, such as determining the administration of chemotherapeutic drugs and/or molecular targeted drugs.
Another aspect of the present invention is to provide a mathematical model for predicting the efficacy of a transformation therapy for metastatic colorectal cancer, which contains 10 gene loci in the above gene set as variables.
Each of the loci has a corresponding weighting factor (or weighting factor, weighting value).
In one embodiment, the mathematical model for predicting the therapeutic effect of the transformation therapy of metastatic colorectal cancer is a logistic regression model represented by the following formula (1):
p in formula (1) is score; b0 is a constant term; b1-b10 are the expression levels of 5hmC of the 10 gene loci, namely b1 corresponds to C18orf42, b2 corresponds to OR10V1, b3 corresponds to ATOH1, b4 corresponds to AC140061.12, b5 corresponds to TMEM26, b6 corresponds to KRTAP4-2, b7 corresponds to PDK4, b8 corresponds to SPAG11B, b9 corresponds to SEC31B, b10 corresponds to LILRA 5; x1-x10 are weight factors corresponding to the 10 gene loci, respectively, i.e., x1 is a weight factor for C18orf42, x2 is a weight factor for OR10V1, x3 is a weight factor for ATOH1, x4 is a weight factor for AC140061.12, x5 is a weight factor for TMEM26, x6 is a weight factor for KRTAP4-2, x7 is a weight factor for PDK4, x8 is a weight factor for SPAG11B, x9 is a weight factor for SEC31B, and x10 is a weight factor for LILRA 5.
Preferably, the expression level of 5hmC at the above 10 loci is a value measured by 5hmC sequencing technology (Nano-hmC-Seal), which is a value after normalization treatment; b0 is-40.34632132; x1 is 0.848169375, x2 is 2.283509231, x3 is 0.476112134, x4 is 0.520679714, x5 is 0.182230579, x6 is 1.725908436, x7 is 0.16735573, x8 is 0.007476752, x9 is 0.469213179, x10 is 0.561901204, and when P ≧ 0.5, resistance to transformation therapy is provided; when P is less than 0.5, the composition is effective for transformation therapy.
As a specific application of the above-mentioned mathematical model for predicting the therapeutic effect of metastatic colorectal cancer transformation, it is, for example, inputted in the form of computer software (computer program) to an information processing module of a gene testing apparatus such as a gene sequencer or inputted to an information processing module of a blood testing apparatus.
Moreover, the mathematical model for predicting the therapeutic effect of the transformation therapy of the metastatic colorectal cancer is input into an intelligent medical system or a computer of a doctor in the form of computer software (computer program), so as to help the doctor judge a transformation therapy scheme, guide the administration of a molecular targeted drug and implement precise medical treatment.
The gene set provided by the invention is a characteristic gene locus for distinguishing the transformation therapy sensitivity from the transformation therapy primary drug resistance in a human genome, so that a prediction model constructed based on the gene set can effectively distinguish the gene types of patients. The prediction model is applied to the identification of 35 patients with colorectal cancer at stage IV, which are accompanied by liver metastasis and can not be resected by liver metastasis, by liquid biopsy, the verification sensitivity and specificity are respectively 86.7% and 95%, and the detection result has high repeatability.
Drawings
FIG. 1 shows the results of detecting the 5hmC expression level of 20000 residual gene locus of the whole genome by using the Nano-hmC-Seal technique, wherein red dots represent one patient in the group of patients with drug resistance for transformation therapy; blue dots represent one patient of the transformation-sensitive group. Wherein, A in figure 1 is a result graph showing the 5hmC expression level of 20000 residual gene loci of the whole genome, and the 5hmC expression levels PCI of the whole genome of the transformation treatment effective group and the transformation treatment primary drug resistance group are not obviously different; in fig. 1, B is a graph showing the results of 5hmC expression levels after PCI discrimination was performed again for 100 gene loci whose expression differences were the most significant, and there was a certain difference between the two groups.
FIG. 2 shows the ROC curve for the prediction model constructed in the present invention, in which AUC takes the value of 0.990, and the sensitivity and specificity are 86.7% and 95%, respectively.
Detailed Description
The prediction means of the curative effect of the transformation therapy of the metastatic colorectal cancer is very limited, and how to accurately screen the transformation therapy beneficial population is very important. The applicant previously established an early diagnosis model of colorectal cancer by using 5hmC detection, and found that 5hmC has characteristic changes in patients with stage IV colorectal cancer.
The problems of low cfDNA content, difficult detection and the like exist in the previous research on cfDNA. The research applies 5hmC sequencing technology (Nano-hmC-Seal) of low-input cfDNA of Shanghai Yien company, and overcomes the defect of low detection rate of DNA apparent modification in the past. However, there is no research on whether a trace 5hmC whole genome sequencing technology can realize prediction of curative effect of metastatic colorectal cancer transformation treatment. Therefore, based on clinical diagnosis and gene sequencing research of the curative effect of the metastatic colorectal cancer transformation treatment, the inventor induces a characteristic gene set based on 5hmC apparent modification, and establishes a model for predicting the curative effect of the hepatic metastasis unresectable metastatic colorectal cancer transformation treatment.
The invention is based on gene sequencing technology, and obtains a characteristic gene locus set which distinguishes the transformation therapy sensitivity and the transformation therapy primary drug resistance in a genome by detecting the 5hmC apparent modification characteristic change in body fluid such as peripheral plasma free DNA and comparing the transformation therapy effectiveness with the transformation therapy primary drug resistance patient peripheral blood 5hmC modification characteristic change difference. The gene set is used as a variable, a model for predicting the curative effect of the transformation therapy of the metastatic colorectal cancer with unresectable liver metastasis is established, and the model can be applied to the first line of clinic.
The term "gene set" herein is a set or combination of genes in the human genome that differ in the 5hmC modification-characteristic changes in all patients with effective transformation therapy as compared to transformation therapy primary resistant patients. Including but not limited to 10 loci of C18orf42, OR10V1, ATOH1, AC140061.12, TMEM26, KRTAP4-2, PDK4, SPAG11B, SEC31B and LILRA 5. For example, the gene set may include the following 100 genes: c18orf42, SPO11, GUCA1C, VTCN1, KRTAP3-2, OR10V1, CEBPB, DMRT 1, ATOH1, AC1, GAS2L 1, GP 1, RP1, PCK1, RIMBP 1, SLC9A 1, SPACA1, NPHS 1, SYNGAP1, SULT1C 1, ISL1, OR1S 1, TMPRSS11 1, SGPP 1, MUC 1, NCAM1, TMTAP 1, Match1, STK32 1, MYOZ 1, KCNC 1, PCDNAP 1, GABRA 1, KCTD1, AQP12, KRTAP 1-2, TEPOI, KRST 1, KR 1, FBN 1, FB 1, 1, 1-1, 685.
In this context, the terms "gene locus" or "gene" are used interchangeably to mean the same meaning, and particularly a gene that can exhibit an apparent modification of 5hmC (or a5 hmC-modified gene).
Herein, the terms "5 hmC appearance modification", "5 hmC appearance modification level", "5 hmC content", "5 hmC expression level" and "5 hmC level" mean the same meaning, and may be used interchangeably to refer to the degree to which a gene is 5 hmC-modified.
As for the construction method of the metastatic colorectal cancer transformation treatment efficacy prediction model, 5hmC high-throughput sequencing technology can be used for comparing the transformation treatment effectiveness with the transformation treatment primary drug-resistant patient peripheral plasma free DNA whole genome 5hmC expression difference, and Deseq and other methods are used for searching partial gene sites with the most obvious expression difference as construction markers of the efficacy prediction model, so that the metastatic colorectal cancer transformation treatment efficacy prediction model is constructed. For example, using high throughput sequencing results to obtain the above listed 100 gene loci, taking factors that may affect 5hmC content as co-variables, and obtaining the weighting coefficients of each gene marker through logistic regression and elastic network regularization, the model is established as shown in the following formula (2):
p in formula (2) is score; b0 is a constant term; b1, b2 and … … bk represent the expression level of 5hmC at each gene locus (k max 100); x1, x2, … … xk are weighting factors corresponding to the respective gene loci, respectively.
It will be apparent to those skilled in the art that the model for predicting the efficacy of the treatment for transforming metastatic colorectal cancer may be in the form of other mathematical models.
The mathematical model of the present invention can be input to the information processing module of a gene testing device, such as a gene sequencer, or to the information processing module of a blood testing device, by programming, in the form of a mathematical software package; or may be entered into a cloud server to form a shareable intelligent medical system, or into a physician's computer, and the operation of these devices may generate guidelines for transforming treatment regimens, such as determining whether a patient is eligible to use a molecularly targeted drug such as cetuximab or bevacizumab.
Clinical practice proves that the prediction model for the curative effect of the metastatic colorectal cancer transformation therapy used for liquid biopsy has the following advantages:
1. the invention is verified in the building module, the detection sensitivity and specificity are respectively 86.7 percent and 95 percent, and the area under the AUC curve of the internal verification is 0.990. At present, in the colorectal cancer research field, no report of liquid biopsy for predicting the efficacy of transformation therapy is available. In addition, the model can be continuously optimized, and the prediction sensitivity and specificity are further improved.
2. The model has the characteristics of noninvasive and convenient detection, only 10ml of peripheral venous blood of a patient needs to be collected, the collection process has no obvious difference with conventional blood examination, 5hmc detection of a whole genome can be completed, and the compliance of the patient can be obviously improved compared with the existing whole exon sequencing and human-derived transplantation tumor; the prediction of the conversion treatment curative effect is realized in a one-stop mode through a prediction model, and the stability is high.
3. The 5hmC detection method adopted by the invention is a mature gene sequencing method such as Nano-hmC-Seal, and the repeatability of the detection result is high. In addition, the detection cost may be reduced again after further model optimization (e.g., reduction of the number of gene variables), and so on.
In order to make the present invention more comprehensible, embodiments accompanying with the drawings are described in detail below. It will be understood by those skilled in the art that the following examples are only for illustrating the feasibility of the present invention and are not intended to limit the present invention.
All percentages referred to in the examples refer to mass percentages unless otherwise indicated (e.g., explicitly indicated as percentages or ratios).
Example 1 screening for genes that differ between efficacy of transformation therapy and Primary drug resistance of transformation therapy
1.1 screening 35 patients with colorectal cancer at the secondary Zhongshan Hospital at the university of Compound Dan, all with liver metastases, and unresectable liver metastases. In the case of informed consent, 10ml of peripheral venous blood was collected separately for gene testing. The detection process cooperates with Shanghai Yi Bien science and technology Limited, and the gene detection is carried out by adopting a low-input DNA 5hmC sequencing technology Nano-hmC-Seal. After transformation treatment, 20 patients were diagnosed as transformation-therapeutically effective group and 15 patients were diagnosed as transformation-therapeutically primary resistant group.
1.2 the 5hmC expression level of 20000 remaining gene loci of the whole genome is detected, as shown in A in figure 1, there is no obvious difference between the two groups of 5hmC expression levels in the whole genome and PCI. Then, we compare the expression difference of 5hmC of each gene locus in two groups by using a Deseq method, and select the first 100 gene loci (p < 0.028) with the largest difference, which are: c18orf42, SPO11, GUCA1C, VTCN1, KRTAP3-2, OR10V1, CEBPB, DMRT 1, ATOH1, AC1, GAS2L 1, GP 1, RP1, PCK1, RIMBP 1, SLC9A 1, SPACA1, NPHS 1, SYNGAP1, SULT1C 1, ISL1, OR1S 1, TMPRSS11 1, SGPP 1, MUC 1, NCAM1, TMTAP 1, Match1, STK32 1, MYOZ 1, KCNC 1, PCDNAP 1, GABRA 1, KCTD1, AQP12, KRTAP 1-2, TEPOI, KRST 1, KR 1, FBN 1, FB 1, 1, 1-1, 685.
1.3 Using the above 100 selected gene loci, two groups were again distinguished by PCI, and some difference was observed between the two groups, as shown in FIG. 1B.
Example 2 establishing a mathematical model for predicting the efficacy of a metastatic colorectal cancer transformation therapy
2.1, 100 gene loci obtained by screening in the example 1 are utilized to establish a prediction model of the curative effect of the transformation treatment of the metastatic colorectal cancer. The weighting coefficients of the genes are obtained through logistic regression and elastic network regularization, and the established model is shown as the following formula (2):
wherein P is the score; b0 is a constant term; b1, b2 and … … bk represent the expression level of 5hmC at each gene locus (k max 100); x1, x2, … … xk are weighting factors corresponding to the respective gene loci, respectively.
2.2 the results of the test were inputted for 20 transformation therapy-effective patients and 15 transformation therapy-primary drug-resistant patients out of the above 35 patients. Interesting gene loci include 10, which are: c18orf42, OR10V1, ATOH1, AC140061.12, TMEM26, KRTAP4-2, PDK4, SPAG11B, SEC31B, and LILRA 5.
Actually outputting 1 constant term and 10 meaningful gene terms b1, b2 and … … bk in the modeling process, and obtaining a diagnostic model as follows:
wherein P is the score; b0 is a constant term; b1-b10 are the expression levels of 5hmC of the 10 gene loci, namely b1 corresponds to C18orf42, b2 corresponds to OR10V1, b3 corresponds to ATOH1, b4 corresponds to AC140061.12, b5 corresponds to TMEM26, b6 corresponds to KRTAP4-2, b7 corresponds to PDK4, b8 corresponds to SPAG11B, b9 corresponds to SEC31B, b10 corresponds to LILRA 5; x1-x10 are weight factors corresponding to the 10 gene loci, respectively, i.e., x1 is a weight factor for C18orf42, x2 is a weight factor for OR10V1, x3 is a weight factor for ATOH1, x4 is a weight factor for AC140061.12, x5 is a weight factor for TMEM26, x6 is a weight factor for KRTAP4-2, x7 is a weight factor for PDK4, x8 is a weight factor for SPAG11B, x9 is a weight factor for SEC31B, and x10 is a weight factor for LILRA 5.
Specifically, b0 is-40.34632132; x1 is 0.848169375, x2 is 2.283509231, x3 is 0.476112134, x4 is 0.520679714, x5 is 0.182230579, x6 is 1.725908436, x7 is 0.16735573, x8 is 0.007476752, x9 is 0.469213179, x10 is 0.561901204.
The prediction model can calculate and score the 5hmC expression change of each sample, and judge whether the sample is a transformation therapy resistant patient or not according to the score value.
EXAMPLE 3 validation of transformed treatment efficacy prediction mathematical model
3.1 we input the results of the 35 samples into the prediction model and score the results of each sample.
The 15 patients with transformation therapy resistance scored as follows: 0.57, 0.76, 0.48, 0.80, 0.46, 0.66, 0.60, 0.74, 0.64, 0.61, 0.83, 0.76, 0.52, 0.56, 0.83.
The scoring results for 20 patients with effective transformation therapy were as follows: 0.30, 0.18, 0.28, 0.23, 0.10, 0.17, 0.28, 0.24, 0.39, 0.56, 0.22, 0.20, 0.26, 0.37, 0.11, 0.13, 0.31, 0.33, 0.27, 0.29.
The results show that: 13 of 15 transformation therapy drug-resistant patients have a score of more than or equal to 0.50, are predicted to be transformation therapy drug-resistant, and 2 patients have wrong judgments; in 20 patients with effective transformation therapy, 19 scores <0.50, and the transformation therapy was judged to be effective, and 1 patient was judged to be incorrect, and the sensitivity and specificity of the detection were 86.7% and 95%, respectively.
Therefore, the scoring criteria of the above mathematical model for predicting the therapeutic effect of the transformation therapy can be determined as follows: when P is more than or equal to 0.5, the medicine resistance is the transformation treatment; when P is less than 0.5, the composition is effective for transformation therapy.
3.2 the prediction model constructed in example 2 was internally validated by ROC curve. Model scoring results were performed on samples of the constructed model, and the ROC curve is shown in fig. 2. Taking the cutoff value of 0.5, diagnosing that the medicine is a primary drug resistance group for transformation therapy if the result is more than or equal to 0.5, and otherwise, considering that the medicine is a sensitive group for transformation therapy. 13 of 15 patients in the transformation therapy drug resistance group are judged correctly, and 2 patients are judged wrongly; in 20 patients with the drug resistance group, 19 patients were judged correctly, 1 patient was judged incorrectly, the sensitivity (sensitivity) and specificity (specificity) of the internal verification were 86.7% and 95%, respectively, and the area under the AUC curve was 0.990.
The technical scheme of the invention is verified by the above embodiment, and the reliability of the prediction model shown in the formula (1) is proved, so that accurate prediction can be provided for distinguishing the patient with drug resistance in the transformation therapy from the patient with effective transformation therapy. It should be noted that various changes or modifications made by those skilled in the art without departing from the spirit of the invention shall also fall within the scope of the invention.
Claims (10)
1. A gene set for predicting the efficacy of a treatment for metastatic colorectal cancer transformation, comprising the following 10 loci in the human genome: c18orf42, OR10V1, ATOH1, AC140061.12, TMEM26, KRTAP4-2, PDK4, SPAG11B, SEC31B, LILRA5, with an apparent modified level of 5hmC at these gene sites predictive of transformation therapy efficacy.
2. The gene set of claim 1, wherein the transformation therapy is the administration of a chemotherapeutic drug and/or a molecularly targeted drug.
3. The gene set of claim 2, wherein the molecular targeted drug is selected from the group consisting of cetuximab and bevacizumab.
4. The gene set of claim 1, used for establishing a mathematical model for predicting the efficacy of a metastatic colorectal cancer transformation therapy.
5. The gene set of claim 4, wherein the mathematical model for predicting the efficacy of a treatment for metastatic colorectal cancer transformation comprises the 10 gene loci as variables.
6. The gene set of claim 5, wherein each gene locus in the mathematical model for predicting the efficacy of the metastatic colorectal cancer transformation therapy has a corresponding weighting factor.
7. The gene set of claim 6, wherein the mathematical model for predicting the efficacy of the metastatic colorectal cancer transformation therapy is a logistic regression model represented by the following formula (1):
wherein P is the score; b0 is a constant term; b1-b10 are the expression levels of 5hmC at the 10 gene loci respectively; x1-x10 are weight factors corresponding to the 10 gene loci, respectively.
8. The gene set of claim 7, wherein the 5hmC expression of the 10 gene loci is determined by 5hmC sequencing technology; b0 is-40.34632132; x1 is 0.848169375, x2 is 2.283509231, x3 is 0.476112134, x4 is 0.520679714, x5 is 0.182230579, x6 is 1.725908436, x7 is 0.16735573, x8 is 0.007476752, x9 is 0.469213179, x10 is 0.561901204, and
when P is more than or equal to 0.5, the medicine resistance is the transformation treatment; when P is less than 0.5, the composition is effective for transformation therapy.
9. The gene set according to any one of claims 5 to 8, wherein the mathematical model for predicting the efficacy of the treatment for transforming metastatic colorectal cancer is inputted to an information processing module of a genetic testing device or to an information processing module of a blood testing device.
10. The gene set according to any one of claims 5 to 8, wherein the mathematical model for predicting the efficacy of the transformation therapy for metastatic colorectal cancer is entered into an intelligent medical system or into a computer of a physician.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910583680.7A CN110257518B (en) | 2019-07-01 | 2019-07-01 | Gene set for predicting curative effect of metastatic colorectal cancer transformation treatment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910583680.7A CN110257518B (en) | 2019-07-01 | 2019-07-01 | Gene set for predicting curative effect of metastatic colorectal cancer transformation treatment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110257518A CN110257518A (en) | 2019-09-20 |
| CN110257518B true CN110257518B (en) | 2022-08-02 |
Family
ID=67923557
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910583680.7A Active CN110257518B (en) | 2019-07-01 | 2019-07-01 | Gene set for predicting curative effect of metastatic colorectal cancer transformation treatment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110257518B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112034182A (en) * | 2020-09-01 | 2020-12-04 | 复旦大学附属中山医院 | Method and system for predicting colon cancer metastasis |
| CN112280863B (en) * | 2020-11-06 | 2024-01-12 | 南京普恩瑞生物科技有限公司 | Method and kit for targeting drug apatinib effectiveness |
| CN117904327A (en) * | 2024-03-19 | 2024-04-19 | 浙江百迪生物科技有限公司 | Biomarker for specifically detecting humanized genes, primer pair, kit and application of biomarker |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2636984A1 (en) * | 2006-01-11 | 2007-07-19 | Genomic Health, Inc. | Gene expression markers for colorectal cancer prognosis |
| WO2009114836A1 (en) * | 2008-03-14 | 2009-09-17 | Genomic Health, Inc. | Gene expression markers for prediction of patient response to chemotherapy |
| CN102424840A (en) * | 2011-12-20 | 2012-04-25 | 上海市肿瘤研究所 | Urine-based method and kit for diagnosing relapse risk of bladder cancer patient |
| CN107622800A (en) * | 2017-10-11 | 2018-01-23 | 复旦大学附属中山医院 | A kind of mathematical modeling for colorectal cancer hepatic metastases outcome prediction |
| CN109504778A (en) * | 2019-01-11 | 2019-03-22 | 复旦大学附属中山医院 | It is a kind of that model is early diagnosed based on the 5hmC polymolecular marker apparently modified and colorectal cancer |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010053539A2 (en) * | 2008-11-05 | 2010-05-14 | The Texas A&M University System | Methods for detecting colorectal diseases and disorders |
-
2019
- 2019-07-01 CN CN201910583680.7A patent/CN110257518B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2636984A1 (en) * | 2006-01-11 | 2007-07-19 | Genomic Health, Inc. | Gene expression markers for colorectal cancer prognosis |
| WO2009114836A1 (en) * | 2008-03-14 | 2009-09-17 | Genomic Health, Inc. | Gene expression markers for prediction of patient response to chemotherapy |
| CN102424840A (en) * | 2011-12-20 | 2012-04-25 | 上海市肿瘤研究所 | Urine-based method and kit for diagnosing relapse risk of bladder cancer patient |
| CN107622800A (en) * | 2017-10-11 | 2018-01-23 | 复旦大学附属中山医院 | A kind of mathematical modeling for colorectal cancer hepatic metastases outcome prediction |
| CN109504778A (en) * | 2019-01-11 | 2019-03-22 | 复旦大学附属中山医院 | It is a kind of that model is early diagnosed based on the 5hmC polymolecular marker apparently modified and colorectal cancer |
Non-Patent Citations (4)
| Title |
|---|
| DNA Methylation Profiles of Primary Colorectal Carcinoma and Matched Liver Metastasis;Kazuo Konishi et al.;《PLOS ONE》;20111121;第6卷(第11期);第1-9页 * |
| Epigenetic Alternations of MicroRNAs and DNA Methylation Contribute to Liver Metastasis of Colorectal Cancer;Jingwei Liu et al.;《Digestive Diseases and Sciences》;20190102;第1-12页 * |
| 结直肠癌化疗药物相关基因研究进展;胡思钦;《深圳中西医结合杂志》;20171231;第27卷(第23期);第193-195页 * |
| 结直肠癌肝转移诊断和综合治疗指南（2016）;中华医学会外科学分会胃肠外科学组等;《中国实用外科杂志》;20160831;第36卷(第8期);第858-869页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110257518A (en) | 2019-09-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| AU2020264326B2 (en) | Detection and treatment of disease exhibiting disease cell heterogeneity and systems and methods for communicating test results | |
| CN110257518B (en) | Gene set for predicting curative effect of metastatic colorectal cancer transformation treatment | |
| CN105506115B (en) | A kind of DNA library and its application of checkout and diagnosis genetic cardiomyopathies Disease-causing gene | |
| CN108949990A (en) | A kind of kit and method detecting EGFR genetic mutation | |
| CN107663533A (en) | A kind of lung cancer EGFR L858R and 19Del ddPCR detection methods and application | |
| CN106367490A (en) | Method for monitoring cancer cell number of cancer patient by adopting circulating DNA | |
| CN110055331A (en) | A kind of kit and its application for bladder cancer auxiliary diagnosis or screening | |
| Im et al. | A multicenter analysis of nucleic acid quantification using aqueous humor liquid biopsy in retinoblastoma: implications for clinical testing | |
| CN117143983A (en) | Molecular marker for evaluating risk of chronic obstructive pulmonary disease and application thereof | |
| CN115786501B (en) | Enhancer functional site related to colorectal cancer early screening and auxiliary diagnosis and application thereof | |
| WO2022156610A1 (en) | Prediction tool for determining sensitivity of liver cancer to drug and long-term prognosis of liver cancer on basis of genetic testing, and application thereof | |
| CN110317876A (en) | Application of the unstable variation of one group chromosome in preparation diagnosis Huppert's disease, the reagent or kit of assessing prognosis | |
| Li et al. | The machine-learning-mediated interface of microbiome and genetic risk stratification in neuroblastoma reveals molecular pathways related to patient survival. Cancers. 2022; 14 | |
| US20240071628A1 (en) | Database for therapeutic interventions | |
| Liang et al. | A plasma 3-marker microRNA biosignature distinguishes spinal tuberculosis from other spinal destructive diseases and pulmonary tuberculosis | |
| CN107151699A (en) | Detect the kit and method of NUP214 ABL1 gene relative expression quantities | |
| US20240052419A1 (en) | Methods and systems for detecting genetic variants | |
| CN113564259B (en) | NCRNA for detecting lung adenocarcinoma cell cycle progression pathway related gene mutation and prediction model | |
| Chen et al. | Integrated Analysis to Obtain Potential Prognostic Signature in Glioblastoma | |
| Nguyen et al. | PD64-10 impact of health literacy on decision-making for prostate-specific antigen screening in the United States | |
| JP2024507542A (en) | Comprehensive polygenic risk assessment for breast cancer | |
| Johnson et al. | EPCO-27. GLIOMA SINGLE CELL MULTI-OMIC ANALYSES REVEALS REGULATORS OF PLASTICITY AND ADAPTIVE STRESS RESPONSE | |
| CN114921559A (en) | Application of GPNMB gene as noninvasive biomarker in preparation of products for diagnosing hepatic fibrosis and liver cancer diseases | |
| Chen et al. | A Prognostic Model for Predicting Liver Cancer Patients Based On Immune Checkpoint Gene-Related Basement Membrane Genes, And Analyzing Immunity and Potential Drug Candidates | |
| CN116904594A (en) | Marker for prognosis layering of IDH mutant astrocytoma and application thereof |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| EE01 | Entry into force of recordation of patent licensing contract | ||
| EE01 | Entry into force of recordation of patent licensing contract |
Application publication date: 20190920 Assignee: Heli Yihui (Shanghai) Medical Technology Co.,Ltd. Assignor: ZHONGSHAN HOSPITAL, FUDAN University Contract record no.: X2022980027004 Denomination of invention: A gene set used to predict the efficacy of transformation therapy for metastatic colorectal cancer Granted publication date: 20220802 License type: Exclusive License Record date: 20221213 |